Abstract
Speech is our most natural and efficient form of communication and offers a strong potential to improve how we interact with machines. However, speech communication can sometimes be limited by environmental (e.g., ambient noise), contextual (e.g., need for privacy), or health conditions (e.g., laryngectomy), preventing the consideration of audible speech. In this regard, silent speech interfaces (SSI) have been proposed as an alternative, considering technologies that do not require the production of acoustic signals (e.g., electromyography and video). Unfortunately, despite their plentitude, many still face limitations regarding their everyday use, e.g., being intrusive, non-portable, or raising technical (e.g., lighting conditions for video) or privacy concerns. In line with this necessity, this article explores the consideration of contactless continuous-wave radar to assess its potential for SSI development. A corpus of 13 European Portuguese words was acquired for four speakers and three of them enrolled in a second acquisition session, three months later. Regarding the speaker-dependent models, trained and tested with data from each speaker while using 5-fold cross-validation, average accuracies of 84.50% and 88.00% were respectively obtained from Bagging (BAG) and Linear Regression (LR) classifiers, respectively. Additionally, recognition accuracies of 81.79% and 81.80% were also, respectively, achieved for the session and speaker-independent experiments, establishing promising grounds for further exploring this technology towards silent speech recognition.
Highlights
Speech is a natural and efficient form of human communication, and, as such, the research on speech technologies that can foster its use in domains such as Human–Computer Interaction (HCI) is highly relevant
This work aimed to assert if, by resorting to frequency-modulated continuous wave (FMCW) radar, and expanding our preliminary work [13], three core aspects for Silent Speech Interfaces (SSI) development could be tackled with this technology: (a) how capable is radar-based technology of successfully recognizing silent speech, (b) how discrepant can the results get when performing different acquisition sessions for the same participant), and (c) to what extent can inter-speaker models be created by resorting to this technology
Concerning the per-speaker experiment, the average accuracy results of 84.50% using the BAG classifier and 88.30% using the Linear Regression (LR) classifier for both acquisition sessions translate into positive indications of FMCW radar-based technology silent speech recognition (SSR) capabilities, given that a set of thirteen words was considered
Summary
Speech is a natural and efficient form of human communication, and, as such, the research on speech technologies that can foster its use in domains such as Human–Computer Interaction (HCI) is highly relevant. While Automatic Speech Recognition (ASR) is commonly used in HCI environments, as in the case of Amazon’s Alexa and Apple’s Siri [1], there are still some scenarios that cannot take the most out of speech interaction, including situations where privacy is needed, environmental noise is present, silence is required, or in the most extreme cases, when health conditions incapacitate speakers to produce acoustic signals To tackle such scenarios, Silent Speech Interfaces (SSI) emerged as a possible alternative to consider, consisting of the process of speech communication in the absence of an audible/intelligible acoustic signal [2]. The recent market launch of the first mobile phone with radar (Google pixel 4; 2019)
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